DM-Stat: Statistical Challenges in the Search for Dark Matter (18w5095)

Arriving in Banff, Alberta Sunday, February 25 and departing Friday March 2, 2018

Organizers

(Queen's University)

Gianfranco Bertone (University of Amsterdam)

(Yale University)

Roberto Ruiz de Austri (University of Valencia)

Description

The Banff International Research Station will host the "DM-Stat: Statistical Challenges in the Search for Dark Matter" workshop from February 25th to March 2nd, 2018.


One of the biggest puzzles in modern physics is the nature of Dark Matter. This mysterious particle makes up 85% of the matter in the Universe, but is invisible except for its gravitational effect on stars, galaxies, and galactic clusters.
Signals of invisible dark matter in the visible world can come from many directions: gamma rays from far-off galaxies, small signals in specially-designed experiments buried deep in underground laboratories, or through the collision of high-energy particles at the Large Hadron Collider. The goal of this workshop is to bring together the dark matter hunters with statisticians and experts on modern machine learning tools in order to illuminate a consistent characterization of dark matter from theories and data. With interdisciplinary experts and state-of-the-art machine learning tools, we can get one step closer to cracking the enigma of dark matter.



The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).